SINR balancing in the downlink of cognitive radio networks with imperfect channel knowledge

Hanif, Muhammad; Smith, Peter J.; Alouini, Mohamed‐Slim

doi:10.4108/icst.crowncom2010.9199

Cited by 8 publications

(9 citation statements)

References 15 publications

(23 reference statements)

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“…Using the procedure in [30, 31], one can introduce an auxiliary variable

γ \geq 0

to replace the max–min in the objective function with a single‐level maximisation objective via the inclusion of new constraint (11c). This procedure allows us to reformulate problem P2 with the following equivalent representation:

(P 3) : \underset{{\{bold-italicw\}}_{k = 1}^{K}, {\{Δ {bold-italicR}_{m}\}}_{m = 1}^{M}, {\{Δ {bold-italicR}_{I b}\}}_{b = 1}^{B}, γ}{max.} γ

s.t. {SINR}_{m} \geq γ

{\hat{R}}_{m} + Δ_{m} ⪰ 0

\underset{normal∀ m \in G_{k}, normal∀ k \in (}{, 1, \dots, K)}{{∥Δ {bold-italicR}_{m}∥}_{F} \leq ϵ_{m}}

\sum_{k = 1}^{K} {(∥∥, A_{b} w_{k})}^{2} \leq P_{b}

\sum_{i = 1}^{K} {bold-italicw}_{i}^{normalH} ({\hat{bold-italicR}}_{I b} + Δ R) {bold-italicw}_{i} ...

…”

Section: System Model and Problem Statementmentioning

confidence: 99%

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

et al. 2020

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Achieving high quality of service (QOS) at the end‐users while maintaining the low interference power at the primary users is the main goal in underlay cognitive radio networks. This goal becomes a more difficult task in the designing of beamforming vectors where all channels state information (CSIs) in the secondary network, as well as the interference CSIs, are uncertain. This task is addressed in this study using an iterative optimization technique. In this technique, the original CSI problem, which is difficult to solve as a single optimization problem, is instead separated into two sub‐problems. The first subproblem is the interference power problem, which can be solved either sub‐optimally or optimally using Lagrange duality. The second sub‐problem is the QOS problem, which can be solved either sub‐optimally or robustly using non‐monotone spectral projected gradient method. The two sub‐problem solutions are then recombined into a single problem to extract beamforming vectors. Two methods are invoked to extract the beamforming vectors: either the successive convex approximation (SCA) method or the bisection search method. Theoretical analysis and simulation results indicate that the two methods can offer a tradeoff between better QOS (using bisection search method) or less computational complexity (using SCA method)

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“…Using the procedure in [30, 31], one can introduce an auxiliary variable

γ \geq 0

(P 3) : \underset{{\{bold-italicw\}}_{k = 1}^{K}, {\{Δ {bold-italicR}_{m}\}}_{m = 1}^{M}, {\{Δ {bold-italicR}_{I b}\}}_{b = 1}^{B}, γ}{max.} γ

s.t. {SINR}_{m} \geq γ

{\hat{R}}_{m} + Δ_{m} ⪰ 0

\underset{normal∀ m \in G_{k}, normal∀ k \in (}{, 1, \dots, K)}{{∥Δ {bold-italicR}_{m}∥}_{F} \leq ϵ_{m}}

\sum_{k = 1}^{K} {(∥∥, A_{b} w_{k})}^{2} \leq P_{b}

\sum_{i = 1}^{K} {bold-italicw}_{i}^{normalH} ({\hat{bold-italicR}}_{I b} + Δ R) {bold-italicw}_{i} ...

…”

Section: System Model and Problem Statementmentioning

confidence: 99%

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

et al. 2020

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“…The first author has been partly supported by the Graduate School in Electronics, Telecommunications and Automation (GETA), the Riitta and Jorma J. Takanen foundation, the Tauno Tönning foundation and the Walter Ahlström foundation. error modeling approaches have been handled by either worst-case optimization [15,[17][18][19][20][21][22][23][24] or stochastic optimization [16,22]. Singlecell and multi-cell CRNs were considered in [15][16][17][19][20][21][22] and [18,[22][23][24], respectively.…”

Section: Introductionmentioning

confidence: 99%

Robust beamforming with decentralized interference coordination in cognitive radio networks

Pennanen

Tölli

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper considers an underlay cognitive radio network where primary and secondary networks coexist. The optimization target is to minimize the sum power of secondary transmitters while satisfying the worst case minimum SINR constraint for each secondary user (SU) and maximum aggregate interference constraint for each primary user (PU). Imperfect channel state information (CSI) is assumed, and the corresponding CSI errors are bounded by ellipsoids. We propose an alternating direction method of multipliers-based decentralized beamforming algorithm which relies only on local imperfect CSI and limited backhaul signaling. The convergence behavior of the proposed algorithm is studied via numerical examples.Index Terms-Alternating direction method of multipliers, cognitive multi-cell beamforming, underlay cognitive radio systems.

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“…An iterative solution has been proposed based on semidefinite relaxation [29], and if the solution is not rank-one, rank-one approximations [29] have to be used to achieve the beamforming vectors. For the same problem, a method to achieve a rank-one solution with some tolerance is presented in [22]. Therefore, none of the above work guarantee the optimal solution of the problem of maximizing the minimum SINR in an underlay CRN.…”

Section: Introductionmentioning

confidence: 99%

“…Then the optimization variables are designed in such a way that an objective value is maximized while guaranteeing the feasibility of the constraints over the given set of possible values of the parameters. This method has been applied to design the robust beamforming vectors for underlay CRNs in [20][21][22][23][24][25][26], where the channel errors are either norm bounded or bounded by ellipsoids. With the exception of [24] and [26], most of the abovementioned work consider a CRN where a single secondary transmitter (TX) co-exists with a primary network.…”

Section: Introductionmentioning

confidence: 99%

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A robust beamformer design for underlay cognitive radio networks using worst case optimization

Wijewardhana

Codreanu

2014

J Wireless Com Network

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We propose a robust beamforming design for underlay cognitive radio networks where multiple secondary transmitters communicate with corresponding secondary receivers and coexist with a primary network. We consider a scenario where all transmitters have multiple antennas and all primary and secondary receivers are equipped with a single antenna. The main focus is to design the optimal transmit beamforming vectors for secondary transmitters that maximize the minimum of the received signal-to-interference-plus-noise ratios of the cognitive users. The interference powers to the primary receivers are kept below a threshold to guarantee that the performance of the primary network does not degrade due to the secondary network. Imperfect channel state information (CSI) in all relevant channels are considered, and a bounded ellipsoidal uncertainty model is used to model the CSI errors. We recast the problem in the form of semidefinite program and an iterative algorithm based on the bisection method is proposed to achieve the optimal solution. Further, we propose upper and lower bounds for the optimal value of the considered problem, which provides better initialization for the algorithm. Numerical simulations are conducted to show the effectiveness of the proposed method against the non-robust design.

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SINR balancing in the downlink of cognitive radio networks with imperfect channel knowledge

Cited by 8 publications

References 15 publications

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

Robust beamforming with decentralized interference coordination in cognitive radio networks

A robust beamformer design for underlay cognitive radio networks using worst case optimization

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